get_instruments()

get_sources()

get_sources(language = "English (American)")

admins_eng_ws <- get_administration_data(language = "English (American)", form = "WS")
admins_eng_ws

n_distinct(admins_eng_ws$data_id)

## [1] 5520

admins_eng_ws %>% count(age)

ggplot(admins_eng_ws, aes(x = age, y = production)) +
  geom_jitter(colour = "grey", size = 0.5) +
  geom_smooth() +
  labs(x = "Age (months)", y = "Productive vocabulary size")+theme_classic()

admins_russian <- get_administration_data(language = "Russian")
admins_russian

admins_ws <- get_administration_data(form = "WS")
admins_ws

admins <- get_administration_data()
admins

nrow(admins)

## [1] 82055

admins %>% count(language, form)

items_eng_ws <- get_item_data(language = "English (American)", form = "WS")
items_eng_ws

items_eng_ws %>% distinct(type)

items_eng_ws %>% distinct(category)

items_eng_ws %>% distinct(lexical_category)

items <- get_item_data()
items

items %>% count(language, form)

ids <- items_eng_ws %>%
  filter(definition %in% c("dog", "cat")) %>%
  pull(item_id)

get_instrument_data(language = "English (American)", form = "WS", items = ids)

get_instrument_data(language = "English (American)", form = "WS",
                    items = ids, administrations = TRUE, iteminfo = TRUE)

twos <- admins_eng_ws %>% filter(age == 24)
dog_cat <- items_eng_ws %>% filter(definition %in% c("dog", "cat"))

get_instrument_data(language = "English (American)", form = "WS", items = dog_cat$item_id,
                    administrations = twos, iteminfo = dog_cat)

Exercises

1. Compute and plot median productive vocabulary size (as proportion of total words) over age in each language. Limit to WS data for children 16-30 months old (hint: left_join and facet_wrap are likely to be helpful).

2. For English WS data, compute and plot the proportion of children that produce each word in the “toys” category at each age.

3. (Bonus: Do the same thing as in 2 but separately for girls and boys.)

Exercise 1

items 

Exercise 1

items %>%
  filter(form == "WS", type == "word") 

Exercise 1

items %>%
  filter(form == "WS", type == "word") %>%
  group_by(language) 

Exercise 1

items %>%
  filter(form == "WS", type == "word") %>%
  group_by(language) %>%
  summarise(words = n()) 

Exercise 1

items %>%
  filter(form == "WS", type == "word") %>%
  group_by(language) %>%
  summarise(words = n()) ->
  num_words

Exercise 1

items %>%
  filter(form == "WS", type == "word") %>%
  group_by(language) %>%
  summarise(words = n()) ->
  num_words

Exercise 1

items %>%
  filter(form == "WS", type == "word") %>%
  group_by(language) %>%
  summarise(words = n()) ->
  num_words

admins 

Exercise 1

items %>%
  filter(form == "WS", type == "word") %>%
  group_by(language) %>%
  summarise(words = n()) ->
  num_words

admins %>%
  filter(form == "WS", age >= 16, age <= 30) 

Exercise 1

items %>%
  filter(form == "WS", type == "word") %>%
  group_by(language) %>%
  summarise(words = n()) ->
  num_words

admins %>%
  filter(form == "WS", age >= 16, age <= 30) %>%
  left_join(num_words) 

Exercise 1

items %>%
  filter(form == "WS", type == "word") %>%
  group_by(language) %>%
  summarise(words = n()) ->
  num_words

admins %>%
  filter(form == "WS", age >= 16, age <= 30) %>%
  left_join(num_words) %>%
  mutate(prop_vocab = production / words) 

Exercise 1

items %>%
  filter(form == "WS", type == "word") %>%
  group_by(language) %>%
  summarise(words = n()) ->
  num_words

admins %>%
  filter(form == "WS", age >= 16, age <= 30) %>%
  left_join(num_words) %>%
  mutate(prop_vocab = production / words) %>%
  group_by(language, age) 

Exercise 1

items %>%
  filter(form == "WS", type == "word") %>%
  group_by(language) %>%
  summarise(words = n()) ->
  num_words

admins %>%
  filter(form == "WS", age >= 16, age <= 30) %>%
  left_join(num_words) %>%
  mutate(prop_vocab = production / words) %>%
  group_by(language, age) %>%
  summarise(median_vocab = median(prop_vocab)) 

Exercise 1

items %>%
  filter(form == "WS", type == "word") %>%
  group_by(language) %>%
  summarise(words = n()) ->
  num_words

admins %>%
  filter(form == "WS", age >= 16, age <= 30) %>%
  left_join(num_words) %>%
  mutate(prop_vocab = production / words) %>%
  group_by(language, age) %>%
  summarise(median_vocab = median(prop_vocab)) ->
  vocab_summary

Exercise 1

ggplot(vocab_summary, aes(x = age, y = median_vocab)) 

Exercise 1

ggplot(vocab_summary, aes(x = age, y = median_vocab)) +
  facet_wrap(~language, ncol = 7) 

Exercise 1

ggplot(vocab_summary, aes(x = age, y = median_vocab)) +
  facet_wrap(~language, ncol = 7) +
  geom_point(size = 0.6) 

Exercise 1

ggplot(vocab_summary, aes(x = age, y = median_vocab)) +
  facet_wrap(~language, ncol = 7) +
  geom_point(size = 0.6) +
  ylim(0, 1) 

Exercise 1

ggplot(vocab_summary, aes(x = age, y = median_vocab)) +
  facet_wrap(~language, ncol = 7) +
  geom_point(size = 0.6) +
  ylim(0, 1) +
  labs(x = "Age (months)", y = "Productive vocabulary size") 

Exercise 1

ggplot(vocab_summary, aes(x = age, y = median_vocab)) +
  facet_wrap(~language, ncol = 7) +
  geom_point(size = 0.6) +
  ylim(0, 1) +
  labs(x = "Age (months)", y = "Productive vocabulary size") +
  theme(strip.text = element_text(size = rel(0.5)))+theme_classic()

— {.build}

Exercise 2

toys <- items_eng_ws %>%
  filter(type == "word", category == "toys")

toys_data <- get_instrument_data(language = "English (American)", form = "WS",
                                 items = toys$item_id,
                                 administrations = admins_eng_ws,
                                 iteminfo = toys) %>%
  mutate(produces = !is.na(value) & value == "produces")

Exercise 2

toys_data 

Exercise 2

toys_data %>%
  group_by(definition, age) 

Exercise 2

toys_data %>%
  group_by(definition, age) %>%
  summarise(prop_produces = sum(produces) / n()) 

Exercise 2

toys_data %>%
  group_by(definition, age) %>%
  summarise(prop_produces = sum(produces) / n()) ->
  toys_summary

Exercise 2

ggplot(toys_summary, aes(x = age, y = prop_produces)) 

Exercise 2

ggplot(toys_summary, aes(x = age, y = prop_produces)) +
  facet_wrap(~definition, ncol = 6) 

Exercise 2

ggplot(toys_summary, aes(x = age, y = prop_produces)) +
  facet_wrap(~definition, ncol = 6) +
  geom_smooth() 

Exercise 2

ggplot(toys_summary, aes(x = age, y = prop_produces)) +
  facet_wrap(~definition, ncol = 6) +
  geom_smooth() +
  labs(x = "Age (months)", y = "Proportion of children producing")+theme_classic()

Exercise 3

toys_data 

Exercise 3

toys_data %>%
  filter(!is.na(sex)) 

Exercise 3

toys_data %>%
  filter(!is.na(sex)) %>%
  group_by(definition, age, sex) 

Exercise 3

toys_data %>%
  filter(!is.na(sex)) %>%
  group_by(definition, age, sex) %>%
  summarise(prop_produces = sum(produces) / n()) 

Exercise 3

toys_data %>%
  filter(!is.na(sex)) %>%
  group_by(definition, age, sex) %>%
  summarise(prop_produces = sum(produces) / n()) ->
  toys_summary_sex

Exercise 3

ggplot(toys_summary_sex, aes(x = age, y = prop_produces)) 

Exercise 3

ggplot(toys_summary_sex, aes(x = age, y = prop_produces)) +
  facet_wrap(~definition, ncol = 6) 

Exercise 3

ggplot(toys_summary_sex, aes(x = age, y = prop_produces)) +
  facet_wrap(~definition, ncol = 6) +
  geom_smooth(aes(colour = sex), se = FALSE) 

Exercise 3

ggplot(toys_summary_sex, aes(x = age, y = prop_produces)) +
  facet_wrap(~definition, ncol = 6) +
  geom_smooth(aes(colour = sex), se = FALSE) +
  scale_colour_ptol(name = "") 

Exercise 3

ggplot(toys_summary_sex, aes(x = age, y = prop_produces)) +
  facet_wrap(~definition, ncol = 6) +
  geom_smooth(aes(colour = sex), se = FALSE) +
  scale_colour_ptol(name = "") +
  labs(x = "Age (months)", y = "Proportion of children producing")+theme_classic()

fit_aoa(toys_data) %>% select(definition, aoa)

fit_aoa(toys_data, method = "glmrob", proportion = 0.8) %>% select(definition, aoa)

get_crossling_items()

get_crossling_data(uni_lemmas = "dog")

Wordbank
– wordbank.stanford.edu
– github.com/langcog/wordbankr
– langcog.github.io/wordbankr
– mb-cdi.stanford.edu
Citation: Frank, M. C., Braginsky, M., Yurovsky, D., & Marchman, V. A. (2017). Wordbank: An open repository for developmental vocabulary data. Journal of Child Language, 44(3), 677-694.

childes-db
– childes-db.stanford.edu/
– github.com/langcog/childesr
– childes.talkbank.org
Citation: Sanchez, A., Meylan, S. C., Braginsky, M., MacDonald, K. E., Yurovsky, D., & Frank, M. C. (2019). childes-db: A flexible and reproducible interface to the Child Language Data Exchange System. Behavior Research Methods, 1-14.

This presentation
– github.com/mikabr/acq-tools
– mikabr.github.io/acq-tools

Contact: mikabr@mit.edu, mcfrank@stanford.edu